Prognostic and predictive molecular markers for breast cancer commonly used in clinical practice include Ki67, ER, PR, Her2, and mitotic index (MI). Of these markers, Ki67 proliferation index (KI), is a universal independent prognostic marker, and MI is an integral element of the current tumor grading system. Unfortunately, these markers have not achieved sufficient reproducibility and reliability to accurately predict disease prognosis and aggressiveness. The limited risk-predictive power of currently-available clinical prognosticators (KI and MI) is the critical barrier preventing accurate patient stratification for optimal therapeutic decision-making. In diagnostic pathology, KI and MI are essentially derived from non-overlapping microscopic fields and are on disparate scales, owing to which they are incomparable and valuable information is lost. Our objective is to bring a tumor's KI and MI on the same measurement scale so as to more accurately harness their true prognostic potential and augment the risk-predictive power of tumor grade. Our central hypothesis predicts that the mitotic frequency within a low-grade tumor drives its ability to acquire highly aggressive and malignant phenotypes. We propose a novel method to rationally integrate KI and MI by defining a combined Mitotic Frequency Risk Index (MFRI) which may serve as a better clinical prognosticator and predictor of metastatic risk associated with a low-grade tumor, than MI. Innovation lies in extracting both KI and MI from the same field by co-staining them immune-fluorescently using anti-Ki67 and anti-phosphohistoneH3 antibody (that stains mitotic cells) to glean an extra layer of relevant information that may enhance the prognostic power of grade. The significance and impact of our approach lies in the broad clinical applicability of MFRI, which would replace MI in the tumor grading system and predict metastatic risk reproducibly and reliably in a variety of tumor types. Our novel paradigm that mitotic frequency among cycling cells (MFRI) is a better prognosticator than MI in low-grade tumors is a groundbreaking concept and holds translational promise in early prediction of tumor behavior. AIM 1 will involve a retrospective review of 5000 medical records of breast cancer patients to (i) evaluate the variable correlation between KI and MI across different grades of breast tumors, and (ii) establish that replacement of MI by the mean MI/mean KI ratio improves prognostic accuracy of tumor grade. AIM 2 will determine the Mitotic Frequency Risk Index (MFRI) for a large cohort of Grade 1 (n=200) and Grade 2 (n=200) breast cancer samples and examine its correlation with clinical outcomes. The successful completion of this project may shift current clinical practice paradigms related to risk prognostication by aiding stratification of patients with Grade 1 and 2 tumors into low- and high-risk categories for personalized medicine. It is likely that MFRI, the novel risk index, may also offer insightful cues into why some low-grade, non-invasive breast cancers transform into aggressive tumors that have the ability to metastasize to distant sites.